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Preeclampsia is a unique complication in the second and third trimesters of pregnancy, but its pathogenesis remains unclear and the early diagnosis and treatment methods are yet to be perfect. Termination of pregnancy at the right time is the only way to prevent its deterioration and avoid adverse pregnancy outcomes. In recent years, with the in-depth research, non-coding RNAs has been found to be involved in many important physiological and pathological processes such as proliferation and apoptosis of trophoblast cells and these non-coding RNAs can regulate each other to form an intricate and competitive endogenous RNA regulatory network. This article will introduce the biological roles of non-coding RNAs in regulating the invasion and proliferation of trophoblast cells in patients with preeclampsia and possible regulatory relationship between non-coding RNAs. Furthermore, the potential clinical value of non-coding RNAs as diagnostic biomarkers for preeclampsia and therapeutic targets are also elaborated.
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Recent studies showed that certain drugs can change regulatory reaction parameters in gene regulatory networks (GRNs) and therefore restore pathological cells to a normal state. A state control framework for regulating biological networks has been built based on attractors and bifurcation theory to analyze this phenomenon. However, the control signal is self-developed in this framework, of which the parameter perturbation method can only calculate the state transition time of cells with single control variable. Therefore, an optimal control method based on the dynamic optimization algorithms is proposed for complex biological networks modeled by nonlinear ordinary differential equations (ODEs). In this approach, dynamic optimization problems are constructed based on basic characteristics of the biological networks. Furthermore, using an example of a simple low-dimensional three-node GRN and a complex high-dimensional cancer GRN, MATLAB is utilized to calculate optimal control strategies with either single or multiple control variables. This method aims to achieve accurate and rapid state regulation for biological networks, which can provide a reference for experimental researches and medical treatment.
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Objective: To screen the different mutated somatic genes between primary ovarian cancer and metastatic ovarian carcinoma using the whole exon sequencing data of catalogue of somatic mutations in cancer (COSMIC) database, and to analyze their function and signal pathway. Methods: The whole exon sequencing data of all tumors were downloaded from the COSMIC database, and the whole exon sequencing data of all ovarian cancer were extracted. In the R 3.5.3 environment, mutation rate of each mutated gene in the primary and metastatic ovarian carcinoma samples were performed. The χ2 test or Fisher's exact probability method was used to identify the mutated gene groups which had statistically significant difference in mutation rate. The mutated gene groups were further analyzed for gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment. Results: We found a total of 520 somatic mutations with statistically significant differences in mutation rate between primary ovarian cancer and metastatic ovarian carcinoma tissues, such as transmembrane protease serine 13 (TMPRSS13), Golgi brefeldin A resistance factor 1 (GBF1), Fos-like antigen 2 (FOSL2), mastermind-like 3 (MAML3), etc. Enriched GO function included presynapse organization, dendrite development, cell-cell adhesion via plasma membrane adhesion molecules, and actin binding, and so on. KEGG pathway included regulation of actin cytoskeleton, tricarboxylic acid carrier, and the like. Conclusion: It can provide clues for revealing the metastasis regulation mechanism of ovarian cancer by exploring different mutated gene group between primary ovarian cancer and metastatic ovarian carcinoma and its related functional pathways. The significant mutated gene group may be used as biomarkers for the diagnosis and treatment of ovarian metastatic cancer.
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Hepatitis B virus (HBV) covalently closed circular DNA (cccDNA) is stably maintained in hepatocytes in the form of minichromosome and is considered the most important cause of chronicity of HBV infection, presence of HBV after antiviral therapy, and recurrence of hepatitis after drug withdrawal. However, due to a lack of antiviral regimens targeting cccDNA itself or the formation and transcription of cccDNA, there is an urgent need for treatment strategies targeting cccDNA. With the gradual understanding of epigenetic modification of histones of the cccDNA minichromosome, epigenetic therapy is expected to become a potential therapy for HBV. This article reviews the current status and future directions of HBV DNA methylation and cccDNA-bound histone modification, in order to provide new thoughts for epigenetic therapy for HBV.
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ObjectiveTo establish a microRNA-mRNA differential expression network for alcoholic hepatitis (AH), and to investigate new targets for the diagnosis and treatment of AH. MethodsDifferentially expressed microRNAs and mRNAs between AH patients and normal controls were screened out. Related software including TargetScan, DIANA, MIRDB, PICTAR, and miRWalk 2.0 was used to search for the target genes of differentially expressed microRNA, and a key microRNA-mRNA network was established using the differentially expressed mRNAs that changed in an opposite way to microRNA. The Database for Annotation, Visualization and Integrated Discovery was used for the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses of target genes. The GCBI online software (www.gcbi.com.cn) was used for enrichment analysis of target genes and core network establishment. The GeneMANIA database in Cytoscape software (genemania.org) was used to perform a protein-protein interaction analysis of key target genes. The above three methods were compared in terms of the search for key pathways involved in the development of AH. ResultsA key microRNA-mRNA network was established with 5 differentially expressed microRNAs including hsa-mir-21-5p, hsa-mir-148a-3p, and hsa-mir-30e-5p and 51 target genes including collagen type IV alpha 1 chain (COL4A1), thrombospondin-2 (THBS2), and integrin alpha 6 (IGTA6). A protein-protein interaction network of key target genes was established. The GO analysis and various pathway analyses showed that the PI3K-Akt pathway and local adhesion were closely associated with AH. ConclusionDuring the development of AH, there are complex interactions between the related proteins of key target genes. COL4A1 and THBS2 may promote the development of AH by activating ITGA6 to regulate the PI3K-Akt pathway and the process of local adhesion. The establishment of the microRNA-mRNA network reveals the key links in the development of AH and highlights the focus of research. The discovery of the genes associated with the PI3K-Akt pathway in AH is expected to provide new targets for the diagnosis and treatment of AH.
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ObjectiveTo establish a microRNA-mRNA differential expression network for alcoholic hepatitis (AH), and to investigate new targets for the diagnosis and treatment of AH. MethodsDifferentially expressed microRNAs and mRNAs between AH patients and normal controls were screened out. Related software including TargetScan, DIANA, MIRDB, PICTAR, and miRWalk 2.0 was used to search for the target genes of differentially expressed microRNA, and a key microRNA-mRNA network was established using the differentially expressed mRNAs that changed in an opposite way to microRNA. The Database for Annotation, Visualization and Integrated Discovery was used for the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses of target genes. The GCBI online software (www.gcbi.com.cn) was used for enrichment analysis of target genes and core network establishment. The GeneMANIA database in Cytoscape software (genemania.org) was used to perform a protein-protein interaction analysis of key target genes. The above three methods were compared in terms of the search for key pathways involved in the development of AH. ResultsA key microRNA-mRNA network was established with 5 differentially expressed microRNAs including hsa-mir-21-5p, hsa-mir-148a-3p, and hsa-mir-30e-5p and 51 target genes including collagen type IV alpha 1 chain (COL4A1), thrombospondin-2 (THBS2), and integrin alpha 6 (IGTA6). A protein-protein interaction network of key target genes was established. The GO analysis and various pathway analyses showed that the PI3K-Akt pathway and local adhesion were closely associated with AH. ConclusionDuring the development of AH, there are complex interactions between the related proteins of key target genes. COL4A1 and THBS2 may promote the development of AH by activating ITGA6 to regulate the PI3K-Akt pathway and the process of local adhesion. The establishment of the microRNA-mRNA network reveals the key links in the development of AH and highlights the focus of research. The discovery of the genes associated with the PI3K-Akt pathway in AH is expected to provide new targets for the diagnosis and treatment of AH.
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Background: Helicobacter pylori (Hp) infection is the leading cause of chronic gastritis, duodenal ulcer, and gastric cancer, and is associated with a variety of extragastrointestinal diseases. However, the molecular pathogenic mechanism of Hp and its interaction with the host are still unclear. Aims: To provide clues for understanding the interaction between Hp and host and the Hp-associated diseases through the establishment of a miRNA-mRNA regulatory network and bioinformatics analysis. Methods: Datasets from Gene Expression Omnibus (GEO) were used to determine the differentially expressed miRNAs and mRNAs in patients with Hp infection. Intersection of differentially expressed mRNAs and targeted mRNAs of the differentially expressed miRNAs was identified to form a miRNA-mRNA regulatory network. Gene ontology (GO)/KEGG enrichment analyses were performed, a protein-protein interaction network was constructed and the possible Hp-associated diseases were analyzed. Results: Gene set enrichment analysis revealed that the mostly enriched gene set was chemokine receptors bind chemokines. Using disease-related analysis of 28 core node genes in protein-protein interaction network, Hp infection was found to be associated with cancers and diseases of digestive, nervous, cardiovascular, respiratory and immune systems. Conclusions: Comprehensive analysis provides a method for predicting pathogen-related human diseases based on miRNA-mRNA regulatory network and points out that Hp infection might increase the risk of many extragastrointestinal diseases.
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Background: MicroRNAs (miRNAs) are negative regulators of gene expression in various eukaryotes and play roles in RNA silencing and post-transcriptional regulation. Gene expression is affected by miRNAs dysregulation in almost all types of malignancies. Aims: To explore the core miRNAs regulatory network of gastric cancer, and to provide a theoretical basis for the analysis of molecular mechanism of miRNAs in the development of gastric cancer. Methods: MiRNAs and mRNA expression profile microarray were used to screen the differentially expressed miRNAs and mRNA. MiRWalk 2.0 was used to predict miRNAs-mRNA interactions, cross-matching with genes were selected by expression profile microarray to define the core differentially expressed miRNAs; and miRNAs-mRNA regulatory network was constructed. GO analysis and KEGG analysis were performed to analyze the targeted gene. Results: Twenty-one up-regulated miRNAs and 36 down-regulated miRNAs in gastric cancer were screened by expression profile microarray. After cross-matching, 1 042 low-expressed genes and 711 high-expressed genes were found, and 10 core miRNAs-mRNA regulatory networks were finally defined. The differentially expressed genes regulated by the core miRNAs were mainly involved in tumor-related pathways, and the high-expressed genes were mainly enriched in 9 signaling pathways such as ECM-receptor interaction, while the low-expressed genes were mainly enriched in 5 signaling pathways such as neuroactive ligand-receptor interaction. GO analysis showed that the up-regulated genes involved 315 functions and the down-regulated genes involved 88 functions, of which extracellular matrix organization was the most relevant. Conclusions: MiRNAs-mRNA regulatory network analysis based on bioinformatics provides a new perspective for gastric cancer research, which helps to systematically elucidate the molecular mechanism of miRNAs in the development of gastric cancer. It could provide a theoretical basis for the screening of biomarkers and the precise target selection for drug treatment of gastric cancer.
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Clostridium difficile (C.difficile) infection is the leading cause of hospital-acquired diarrhea and pseudomembranous colitis.C.difficile-associated disease (CDAD) is mediated mainly by two bacterial toxins, TcdA and TcdB, which cause the diarrhea, as well as colitis and even intestinal necrosis.It has been indicated that level of C.difficile toxin is an important factor influencing the clinical phenotype of CDAD, however, the exact association between toxin and clinical phenotype remains unclear.In this article, we summarized the clinical phenotype of CDAD, the structure, function and regulatory mechanism of C.difficile toxin and discussed the relationship between C.difficile toxin and clinical phenotype, which may help to understand the pathogenic mechanism and provide possible therapeutic target for C.difficile infection.
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BACKGROUND: Traditional medicines have been leveraged for the treatment and prevention of obesity, one of the fastest growing diseases in the world. However, the exact mechanisms underlying the effects of traditional medicine on obesity are not yet fully understood. METHODS: We produced the transcriptomes of epididymal white adipose tissue (eWAT), liver, muscle, and hypothalamus harvested from mice fed a normal diet, high-fat-diet alone, high-fat-diet together with green tea, or a high-fat-diet together with Taeumjowitang, a traditional Korean medicine. RESULTS: We found tissue-specific gene expression patterns as follows: (i) the eWAT transcriptome was more significantly altered by Taeumjowitang than by green tea, (ii) the liver transcriptome was similarly altered by Taeumjowitang and green tea, and (iii) both the muscle and hypothalamus transcriptomes were more significantly altered by green tea than Taeumjowitang. We then applied integrated network analyses, which revealed that functional networks associated with lymphocyte activation were more effectively regulated by Taeumjowitang than by green tea in the eWAT. In contrast, green tea was a more effective regulator of functional networks associated with glucose metabolic processes in the eWAT. CONCLUSIONS: Taeumjowitang and green tea have a differential tissue-specific and pathway-specific therapeutic effect on obesity.
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Animals , Mice , Adipose Tissue, White , Diet , Gene Expression , Gene Regulatory Networks , Glucose , Hypothalamus , Liver , Lymphocyte Activation , Medicine, Traditional , Metabolism , Obesity , Sequence Analysis, RNA , Tea , TranscriptomeABSTRACT
More than 50％ microRNAs (miRNAs) are located in tumor-associated genome of amplification region or fragile site,which may also act as oncogenes or tumor suppressor gene (TSG).Recently,researches show that the expression of miR-449 is lower in human gastric,lung and ovarian cancer,and may act as TSG.The abnormal expression of miR-449 plays a pivotal role in carcinogenesis and progression,and elucidating its function and regulatory,mechanism can provide valuable diagnostic,prognostic biomarker for cancer management.
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BACKGROUND:RACK1 is strongly associated with the occurrence and development of oral squamous cel carcinoma. However, the occurrence and development of tumor do not depend on a gene or protein, but a long-term complex process of a network structure of multiple genes and multiple molecules, multi-step, multi-stage joint action. Synergism between tumor genes promotes the formation and development of tumor cel s. Therefore, we cannot limit on a single gene or protein to discover the action mechanism of oral squamous cel carcinoma, but should pay attention on signaling network path related to differential protein or gene, investigate the alterations in related protein or gene expression in the whole signaling pathway, and analyze the action mechanism of the interaction of these molecules. OBJECTIVE:To screen differential genes related to oral squamous cel carcinoma, construct an interaction network through bioinformatics using STRING database, and provide clues for future tests. METHODS:In accordance with our previous classic proteomics results and microarray results of oral squamous cel carcinoma, genes with consistent expression and big differences were selected as differential genes. The differential genes were inputted into the database of STRING to find the possible relationship among the protein subunits and to construct network structure of their interaction. RESULTS AND CONCLUSION:The 19 differential proteins of oral squamous cel carcinoma construct a complicated net work, and the differential proteins interact through these networks. GNB2L1-encoded RACK1 is a node protein and interacts with other differential proteins via WD40 repeated protein (number COG2319) andβ-G protein subunit (number KOG0279). WD40 repeated protein (number COG2319) interacts with 5 differential proteins directly and constructs 10 interacting pathways.β-G protein subunit (number KOG0279) interacts with 8 differential proteins directly, which has 11 interacting pathways. We make a network structure picture based on the interaction of these 19 differential genes by the analysis of the STRING database. The results show that the two subunits of RACK1 protein have direct interaction with 8 differential proteins and have 18 interaction pathways on the picture. As a result, RACK1 is the core protein of the network, suggesting RACK1 is the key node protein in oral squamous cel carcinoma.
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Adaptive systems frequently incorporate complex structures which can arise spontaneously and which may be nonadaptive in the evolutionary sense. We give examples from phase transition and fractal growth to develop the themes of cooperative phenomena and pattern formation. We discuss RNA interference and transcriptional gene regulation networks, where a major part of the topological properties can be accounted for by mere combinatorics. A discussion of ensemble approaches to biological systems and measures of complexity is presented, and a connection is established between complexity and fitness.
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OBJECTIVES: Recently, comparison of drug responses on gene expression has been a major approach to identifying the functional similarity of drugs. Previous studies have mostly focused on a single feature, the expression differences of individual genes. We provide a more robust and accurate method to compare the functional similarity of drugs by diversifying the features of comparison in gene expression and considering the sample dependent variations. METHODS: For differentially expressed gene measurement, we modified the conventional t-test to normalize variations in diverse experimental conditions of individual samples. To extract significant differentially co-expressed gene modules, we searched maximal cliques among the co-expressed gene network. Finally, we calculated a combined similarity score by averaging the two scaled scores from the above two measurements. RESULTS: This method shows significant performance improvement in comparison to other approaches in the test with Connectivity Map data. In the test to find the drugs based on their own expression profiles with leave-one-out cross validation, the proposed method showed an area under the curve (AUC) score of 0.99, which is much higher than scores obtained with previous methods, ranging from 0.71 to 0.93. In the drug networks, we could find well clustered drugs having the same target proteins and novel relations among drugs implying the possibility of drug repurposing. CONCLUSIONS: Inclusion of the features of a co-expressed module provides more implications to infer drug action. We propose that this method be used to find collaborative cellular mechanisms associated with drug action and to simply identify drugs having similar responses.
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Biomarkers, Pharmacological , Drug Repositioning , Gene Expression Regulation , Gene Expression , Gene Regulatory Networks , Methods , TranscriptomeABSTRACT
Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes.
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Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.
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Biology , Dataset , Gene Regulatory Networks , Genome-Wide Association Study , Mass Screening , Oligonucleotide Array Sequence AnalysisABSTRACT
Objective To explore the microRNA expression changes and related characteristics and analyze the corresponding miRNA target genes and their bioinformatics in colorectal cancer with liver metastasis.Methods The fresh specimens of primary CRC were collected in 10 patients during operation,with liver metastasis or without.The miRNA expression levels were compared by miRNA microarray between two groups.Then,target genes were identified using target prediction algorithms.The liver metastasis related miRNA-target networks and gene ontology (GO) bioinformatics analysis were further performed.Results A total of six dysregulated miRNAs were identified in colorectal cancer liver metastasis comparing with no metastasis,including 3 up-regulated miRNAs (miR-224,miR-1236,miR-622) and 3 downregulated miRNAs (miR-155,miR-342-5p,miR-363).miR-224 with most significantly up-regulation played regulation role not only with corresponding target-genes but also downstream genes.Conclusions As a core of the regulation networks,miR-224 can regulate the related gene functional groups simultaneously and asynchronously.It further implements the post-transcriptional regulation and plays a vital role in liver metastasis of colorectal cancer.
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Environmental endocrine disruptors (EED) are pollutants of many exogenous chemicals,which have the potential to disrupt endocrine functions in exposed organisms.The enzymes increasingly involved in the steroid biosynthesis pathway are being recognized as important targets for the actions of various endocrine disrupting chemicals.Interferences with steroid biosynthesis may result in impaired reproduction,alterations in sexual differentiation,sexual development and the development of certain cancers.Aromatase ( CYP19 ) and steroidogenic acute regulatory protein regulated by some transcriptional factors and signalling pathway are considered as the key and rate-limiting enzymes.Given their key role in the formation of steroid hormones,gene regulatory networks of enzymes related to steroidogenesis are gaining interest as molecular targets.Differences in genetic background can affect body's sensitivity to EED.This review will provide an overview of the enzymes involved in steroidogenesis,their cellular and molecular regulation,as well as the adverse effect of EED on them.
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Regulation between genes is a dynamic event associated with changes of time and circumstances. Gene regulatory network is a complicated and dynamic system. Time series gene microarray provides a tool for creating dynamic gene regulatory network. In this paper,we review several models of dynamic gene regulatory network based on time series gene expression data, including temporal Boolean network,differential equation,dynamic Bayesian networks,etc.. The advantages and disadvantages of the models were analyzed and the future of the research is predicted.
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Gene regulatory networks(GRN),which focuses on the complex interactions of genes in life,is an important part in the study of the functional genomics and is the frontier of bioinformatics research.Application of gene-chip technique in bioinformatics provides a great number of basic data for the research of GRN.This paper reviews the origin and recent development of GRN,explicates the preconditions and rationales for construction of GRN,and analyzes several classic GRN models: Boolean networks,linear models,non-linear models and Bayesian networks.The rationales,basic algorithms,advantages,disadvantages and applicability of the models are reviewed based on the characteristics of gene-chip data.